This paper presents a multilevel framework for multiple object tracking in simple and complex environments. The foreground object is obtained using Fuzzy Inference System (FIS) to deal with the illumination changes, shadows, repetitive motion of the objects and clutters in the scene. Multiple object tracking is performed using Hungarian Algorithm and Kalman Filter (KF). Kalman Filter provides an optimal estimate of its position at each time step. The optimality is guaranteed if all noise is Gaussian. KF gives better results based on position estimation to avoid occlusion. Hungarian Algorithm is used to find a particular human in successive frames. The mUlti-person tracking is a generalization of the single person tracker. We assume that the motion of each person is independent of others.For each object in the scene, a separate KF is initialized and models its trajectory.
In this paper, a novel approach is proposed to track humans for video surveillance using multiple cameras and video stitching techniques. SIFT key points are extracted from all camera inputs. Using k-d tree algorithm, all the key points are matched and random sample consensus (RANSAC) is used to identify the match correspondence among all the matched points. Homography matrix is calculated using four matched robust feature correspondences, the images are warped with respect to the other images, and the human tracking is performed on the stitched image. To identify the human in the stitched video, background modeling is performed using fuzzy inference system and perform foreground extraction. After foreground extraction, the blobs are constructed around each detected human and centroid point is calculated for each blob. Finally, tracking of multiple humans is done by Kalman filter (KF) with Hungarian algorithm.
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